Toward Large-Scale Test for Certifying Autonomous Driving Software in Collaborative Virtual Environment

Abstract

Virtual simulation environments are widely used to test autonomous driving software by creating highly complex driving scenarios that are non-trivial to set up in a physical environment. However, the current practice of using the virtual test still does not fully utilize its potential to build a much larger scale test. We propose a perspective and research vision to build a large-scale test architecture in which participants collaboratively construct, execute and analyze complex test scenarios at scale in the virtual world. In particular, the architectural concept is built on the existing concept of the Collaborative Virtual Environment (CVE) that has been successfully applied in other domains, such as entertainment or military training applications. The proposed domain-specific architectural requirements extend the CVE to include the following necessary properties - selective sharing and collaboration - to test autonomous driving software. In addition, the test architectural concept is explained as to how a large number of participants interact with each other collaboratively to build and execute diverse test scenarios at scale. Finally, we explain the new research directions to make this test architectural concept realized for testing autonomous driving software.

Keywords

• Certification
• collaborative virtual environment
• driving scenario selection
• software safety
• testing autonomous driving software
• test automation